| rsvp | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| trial_type | object/animal | object/animal, bird | object/animal, insect | object/body part | object/clothing | object/container | object/container, kitchen utensil | object/container, vehicle | object/decoration | object/electronic device | object/food | object/food, beverage | object/food, dessert | object/food, vegetable | object/fruit | object/furniture | object/kitchen appliance | object/kitchen utensil | object/musical instrument | object/plant | object/sports equipment | object/sports equipment, weapon | object/tool | object/toy | object/vegetable | object/vehicle | object/vehicle, weapon | object/weapon |
| subject | ||||||||||||||||||||||||||||
| 01 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 02 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 03 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 04 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 05 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 06 | 840 | 172 | 93 | 192 | 554 | 503 | 20 | 214 | 160 | 13 | 687 | 92 | 54 | 7 | 307 | 166 | 39 | 60 | 196 | 218 | 82 | 6 | 251 | 129 | 130 | 148 | 7 | 99 |
| 07 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 08 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 09 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 10 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 11 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 12 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 13 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 14 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 15 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 16 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 17 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 18 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 19 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 20 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 21 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 22 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 23 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 24 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 25 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 26 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 27 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 28 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 29 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 30 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 31 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 32 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 33 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 34 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 35 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 36 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 37 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 38 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 39 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 40 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 41 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 42 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 43 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 44 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 45 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 46 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 47 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 48 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 49 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
| 50 | 1500 | 312 | 168 | 348 | 996 | 900 | 36 | 384 | 288 | 24 | 1236 | 168 | 96 | 12 | 564 | 300 | 72 | 108 | 360 | 396 | 144 | 12 | 456 | 228 | 228 | 264 | 12 | 180 |
"""Configuration file for the ds003825 dataset.
Set the `MNE_BIDS_STUDY_CONFIG` environment variable to
"config_ds003825" to overwrite `config.py` with the values specified
below.
Download ds003825 from OpenNeuro: https://github.com/OpenNeuroDatasets/ds003825
"""
import itertools
from mne_bids import get_entity_vals
study_name = 'ds003825'
# bids_root = f'~/mne_data/{study_name}'
bids_root = f'/storage/store2/data/{study_name}'
deriv_root = f'/storage/store2/derivatives/{study_name}/mne-bids-pipeline/'
subjects = sorted(get_entity_vals(bids_root, entity_key='subject'))
# subjects = subjects[:1]
task = 'rsvp'
interactive = False
ch_types = ['eeg']
resample_sfreq = 250.0
epochs_tmin = -0.05
epochs_tmax = 0.6
decim = 2
baseline = (None, 0)
reject = None
# reject = {'eeg': 150e-6}
conditions = ["animal", "food", "body part"]
contrasts = list(itertools.combinations(conditions, 2))
decode = True
# not all runs have the same number of channels
# exclude_subjects = ['002', '003', '004', '005', '018', '019']
# event_repeated = "drop"
run_source_estimation = False
N_JOBS = 20
# N_JOBS = 1
on_error = "debug"
# memory_location = False
Platform: Linux-4.15.0-136-generic-x86_64-with-glibc2.27
Python: 3.9.9 | packaged by conda-forge | (main, Dec 20 2021, 02:41:03) [GCC 9.4.0]
Executable: /data/parietal/store/work/agramfor/mambaforge/bin/python3.9
CPU: x86_64: 88 cores
Memory: 503.8 GB
mne: 1.2.dev0
numpy: 1.21.6 {MKL 2022.0-Product with 1 thread}
scipy: 1.8.1
matplotlib: 3.4.3 {backend=agg}
sklearn: 1.1.2
numba: 0.55.1
nibabel: 3.2.1
nilearn: Not found
dipy: Not found
openmeeg: Not found
cupy: Not found
pandas: 1.3.3
pyvista: 0.32.1 {OpenGL could not be initialized}
pyvistaqt: 0.5.0
ipyvtklink: Not found
vtk: 9.1.0
qtpy: 1.11.2 {PyQt5=5.12.9}
ipympl: Not found
pyqtgraph: Not found
pooch: v1.6.0
mne_bids: 0.12.dev0
mne_nirs: Not found
mne_features: Not found
mne_qt_browser: Not found
mne_connectivity: Not found
mne_icalabel: Not found